alterlab-shap

SHAP values explain model predictions by attributing output changes to individual features using Shapley values, enabling both local and global interpretability. This provides principled explanations for debugging, model validation, and feature engineering.

Yes, you can use SHAP explainers with deep learning and tree-based models. This Skill supports TreeExplainer, DeepExplainer, LinearExplainer, and KernelExplainer to diagnose feature importance, interactions, and fairness across various model architectures.

You can generate beeswarm, waterfall, bar, scatter, heatmap, and force visualizations to diagnose feature importance and interactions. These visualizations interpret SHAP value distributions to facilitate debugging, feature engineering, and model comparison.

The best way to select a SHAP explainer is by matching it to your model architecture: TreeExplainer for tree-based models, DeepExplainer for deep learning, LinearExplainer for linear models, and KernelExplainer for black-box models. This ensures accurate Shapley value computation.

Yes, SHAP supports computing interaction values alongside standard feature attributions. You can calculate both SHAP values and interaction values to diagnose complex feature relationships and fairness across data analytics tasks.